Enriching Knowledge Base by Parse Tree Pattern and Semantic Filter
This paper proposes a simple knowledge base enrichment based on parse tree patterns with a semantic filter. Parse tree patterns are superior to lexical patterns used commonly in many previous studies in that they can manage long distance dependencies among words. In addition, the proposed semantic f...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-09-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/10/18/6209 |
| _version_ | 1797554368278429696 |
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| author | Hee-Geun Yoon Seyoung Park Seong-Bae Park |
| author_facet | Hee-Geun Yoon Seyoung Park Seong-Bae Park |
| author_sort | Hee-Geun Yoon |
| collection | DOAJ |
| description | This paper proposes a simple knowledge base enrichment based on parse tree patterns with a semantic filter. Parse tree patterns are superior to lexical patterns used commonly in many previous studies in that they can manage long distance dependencies among words. In addition, the proposed semantic filter, which is a combination of WordNet-based similarity and word embedding similarity, removes parse tree patterns that are semantically irrelevant to the meaning of a target relation. According to our experiments using the DBpedia ontology and Wikipedia corpus, the average accuracy of the top 100 parse tree patterns for ten relations is 68%, which is 16% higher than that of lexical patterns, and the average accuracy of the newly extracted triples is 60.1%. These results prove that the proposed method produces more relevant patterns for the relations of seed knowledge, and thus more accurate triples are generated by the patterns. |
| first_indexed | 2024-03-10T16:31:02Z |
| format | Article |
| id | doaj.art-c64506458c224a138ef77d977f3c2a56 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T16:31:02Z |
| publishDate | 2020-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-c64506458c224a138ef77d977f3c2a562023-11-20T12:51:32ZengMDPI AGApplied Sciences2076-34172020-09-011018620910.3390/app10186209Enriching Knowledge Base by Parse Tree Pattern and Semantic FilterHee-Geun Yoon0Seyoung Park1Seong-Bae Park2School of Computer Science and Engineering, Kyungpook National University, 80 Daehak-ro, Bukgu, Daegu 41566, KoreaSchool of Computer Science and Engineering, Kyungpook National University, 80 Daehak-ro, Bukgu, Daegu 41566, KoreaDepartment of Computer Science and Engineering, Kyung Hee University, 1732 Deogyeong-daero, Yongin-si 17104, Gyeonggi-do, KoreaThis paper proposes a simple knowledge base enrichment based on parse tree patterns with a semantic filter. Parse tree patterns are superior to lexical patterns used commonly in many previous studies in that they can manage long distance dependencies among words. In addition, the proposed semantic filter, which is a combination of WordNet-based similarity and word embedding similarity, removes parse tree patterns that are semantically irrelevant to the meaning of a target relation. According to our experiments using the DBpedia ontology and Wikipedia corpus, the average accuracy of the top 100 parse tree patterns for ten relations is 68%, which is 16% higher than that of lexical patterns, and the average accuracy of the newly extracted triples is 60.1%. These results prove that the proposed method produces more relevant patterns for the relations of seed knowledge, and thus more accurate triples are generated by the patterns.https://www.mdpi.com/2076-3417/10/18/6209knowledge enrichingparse tree patternsemantic filterword embeddingsemantic relevance |
| spellingShingle | Hee-Geun Yoon Seyoung Park Seong-Bae Park Enriching Knowledge Base by Parse Tree Pattern and Semantic Filter Applied Sciences knowledge enriching parse tree pattern semantic filter word embedding semantic relevance |
| title | Enriching Knowledge Base by Parse Tree Pattern and Semantic Filter |
| title_full | Enriching Knowledge Base by Parse Tree Pattern and Semantic Filter |
| title_fullStr | Enriching Knowledge Base by Parse Tree Pattern and Semantic Filter |
| title_full_unstemmed | Enriching Knowledge Base by Parse Tree Pattern and Semantic Filter |
| title_short | Enriching Knowledge Base by Parse Tree Pattern and Semantic Filter |
| title_sort | enriching knowledge base by parse tree pattern and semantic filter |
| topic | knowledge enriching parse tree pattern semantic filter word embedding semantic relevance |
| url | https://www.mdpi.com/2076-3417/10/18/6209 |
| work_keys_str_mv | AT heegeunyoon enrichingknowledgebasebyparsetreepatternandsemanticfilter AT seyoungpark enrichingknowledgebasebyparsetreepatternandsemanticfilter AT seongbaepark enrichingknowledgebasebyparsetreepatternandsemanticfilter |